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Consumer attitudes toward online video advertisement: YouTube as a platform

Keng-Chieh Yang, Chia-Hui Huang, Conna Yang, Su Yu Yang,
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Keng-Chieh Yang, Chia-Hui Huang, Conna Yang, Su Yu Yang, (2017) "Consumer attitudes toward
online video advertisement: YouTube as a platform", Kybernetes, Vol. 46 Issue: 5, pp.840-853, https://
doi.org/10.1108/K-03-2016-0038
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K
46,5 Consumer attitudes toward
online video advertisement:
YouTube as a platform
840 Keng-Chieh Yang
Department of Information Management, Hwa Hsia University of Technology,
New Taipei City, Taiwan
Chia-Hui Huang
Department of Business Administration, National Taipei University of Business,
Taipei, Taiwan
Conna Yang
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Center for General Education, Ming Chuan University, Taipei, Taiwan, and
Su Yu Yang
Department of Information Management, National Chiao Tung University,
Hsinchu, Taiwan

Abstract
Purpose – Online video advertisement is a wide-ranging phenomenon on the internet that provides huge
opportunities for business enterprises. The revenues of website service providers come primarily from
advertisement. However, it is rare to find research focusing on consumer attitudes toward online video
advertisement. This study aims to investigate consumer attitudes toward advertisement while they are
watching online videos on YouTube.
Design/methodology/approach – The paper followed Brackett and Carr’s (2001) Web Advertising
Attitudes Model and combined it with the theory of reasoned action (TRA) and the flow theory. This study
investigates consideration of the factors affecting attitudes toward advertisement and the influence on
shopping intention and purchase behavior.
Findings – The findings indicate that entertainment, informativeness, irritation and credibility have a
shopping influence on purchase attitudes. Flow, on the other hand, does have an influence on shopping
intention and purchase behavior. The discussion and conclusion have been further discussed.
Originality/value – This study provides a comprehensive model for online video advertisement. This
model was based on Brackett and Carr’s model, combining the users and gratifications theory, TRA and flow
theory to develop an online video advertisement model. Researchers can consider this model as a framework
and use it to capture a more complete picture of the relevant phenomena in their works.
Keywords YouTube, Consumer attitudes, Theory of reasoned action, Flow theory,
Online video advertisement
Paper type Research paper

The previous draft has been published in IEEE International Conference on Industrial Engineering
and Engineering Management (IEEM2014). The authors modified and extended the content based on
some scholars’ suggestions to develop this work. The authors would like to appreciate the
Kybernetes anonymous referees for their useful comments and suggestions which helped to improve the quality
Vol. 46 No. 5, 2017
pp. 840-853 and presentation of this manuscript. Also, special thanks to the Ministry of Science and Technology,
© Emerald Publishing Limited
0368-492X
Taiwan, for financially supporting this research under Grant No. NSC 102-2815-C-146-003-H, MOST
DOI 10.1108/K-03-2016-0038 104-2410-H-146-001 and MOST 104-2410-H-141-016.
 Introduction Online video
According to Internet World Stats, the total number of internet users in the world in 2014 advertisement
exceeded 3 billion (internetworldstats.com, 2014). Moreover, the growth rate this represents in
terms of the number of internet users is about 741 per cent compared year-on-year with the
number of users in 2000 (internetworldstats.com, 2014). An investigation carried out by the
Institute for Information Industry (III) in Taiwan revealed that as of June 2013, the total number
of households connected to a wired broadband network had reached 5.38 million, the number of
internet users could exceed 11 million and the penetration rate of internet usage had reached 48 841
per cent in Taiwan (Find.com, 2014b). Another investigation by the III indicated that the
number of 3G/4G mobile online users has reached 10.5 million, with more than 50 per cent of
the users watching TV programs on video websites, such as YouTube (Find.com, 2014a).
Online video advertisement exerts a wide-ranging influence on the internet, and provides
huge opportunities for business enterprises. The revenues of website service providers come
mostly from advertisement. This study specifically focuses on YouTube, one of the most
well-known online video sites, and aims to address the following research questions:
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RQ1. What are the Web advertising variables that affect customers’ attitudes?
RQ2. Does flow influence the purchase intention and shopping behavior after watching
the online video advertising?
RQ3. Do video consumers’ attitudes influence the shopping intention after watching the
online video advertisement?
This research establishes an online video advertisement attitude model integrating the Web
advertising attitude model developed by Brackett and Carr (2001) model as the basis for
extending their study. Our model also incorporates the theory of reasoned action (TRA) and
flow theory. Ducoffe (1996) indicated that online advertising value is a measure of
advertising effectiveness. His research findings showed the role of advertising value in Web
advertising context and examined the determinants of advertising value. In other words,
when consumers watch online advertising, they may need to know the product information
(informativeness), plus some enjoyment or emotional release (entertainment) and trust of the
product or brand (credibility). In contrast, consumers may not be disturbed by advertising
when they navigate the webpage (irritation). Hsu and Lu (2004) indicated that flow is an
critical predictor of purchase intention in the advertising research model. Flow is a fully
immersed state that people undergo when they act with the environment (Csikszentmihalyi,
1997). Flow is a kind of mental concentration in Web browsing or navigation (Erkan and
Evans, 2016). Hence, flow is an important factor for customers to increase the purchase
intention in e-commerce (Gao and Koufaris, 2006; Yan et al., 2016). It is crucial to identify the
antecedents of advertising attitudes and flow experience more carefully, and to integrate
these variables into a comprehensive model that can provide a clear understanding of how
these factors influence shopping intention and purchase behavior.
This research provides a theoretical understanding and extension of the online
advertising model. Our findings show that the model identifies the crucial factors in terms of
attitudes toward advertisement in online video services, such as YouTube. The findings can
provide for academic and practitioner reference.

Literature review and hypothesis development

Flow
Csikszentmihalyi (1975) defined flow as the holistic sensation that people feel when they act
with total involvement. When people are in flow, they shift into a common mode of
 K experience when they become absorbed in their activity. This mode is characterized by a
46,5 narrowing of the focus of awareness so that irrelevant perceptions and thoughts are filtered
out, by loss of self-consciousness, by a responsiveness to clear goals and unambiguous
feedback and by a sense of control over the environment. Furthermore, Csikszentmihalyi
(1997) characterizes online flow as involving machine interactivity, enjoyment and loss of
self-consciousness, and as being self-reinforcing. Hoffman and Novak (1996) proposed that
842 online flow is a cognitive state experienced during navigation. This cognitive state has been
characterized as an optimal experience that is intrinsically enjoyable. Moreover, Hausman
and Siekpe (2009) pointed out that some researchers view flow as central to human–
computer interactions and have empirically assessed the capacity of flow to explain the use
of computer systems. Some studies demonstrate that flow is a critical predictor of purchase
intention in the advertising research model (Kim and Han, 2014). Flow is a kind of mental
status that people concentrate in online surfing or navigation (Erkan and Evans, 2016).
Hence, flow plays a crucial factor for customers to increase the purchase intention in e-
commerce (Gao and Koufaris, 2006; Yan et al., 2016).
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Attitudes toward internet advertising

Ducoffe (1996) demonstrated that entertainment, informativeness and irritation influenced
attitudes toward Web advertisements. The content (informativeness) and form
(entertainment) of ads are important predictors of their value and are crucial to the
effectiveness of Web advertising, while irritation has negative impacts on viewer attitudes.
Although some researchers have assumed that attitudes is an important construct of
internet advertising (Ducoffe, 1996), Schlosser et al. (1999) thought it also plausible that the
unique characteristics of the internet, when used primarily as an information-providing
medium, might cause the underlying structure of attitudes toward internet advertising to
differ. Their study viewed internet advertisements as more informative and trustworthy.
They found not only that the traditional assessments of advertising effectiveness (i.e.
considering the information and entertainment value) would apply to advertising on the
Web, but also that the advertisement’s utility for making behavioral (purchasing) decisions
would influence viewer attitudes toward internet advertising.
Brackett and Carr (2001) thought that attitudes toward online advertisements is the
aggregation of weighted evaluations of perceived attributes and consequences of products,
and they developed an integrated Web advertising attitude model modified from several
earlier studies. The present research also uses this model as a basis to establish a new model
of attitudes toward online video advertisements.
Informativeness. Informativeness means that “consensus exists with regard to the ability
of advertising to inform consumers of product alternatives”, and accordingly, the satisfying
decision of purchasing can be made (Schlosser et al., 1999). The concept is extended from the
users and gratifications theory (UGT). The UGT is an approach to realizing why and how
people actively seek out specific media to satisfy specific needs. The UGT is an audience-
centered approach to understanding mass communication. It assumes that video viewers
are not passive consumers of media. Rather, these viewers have power over their media
consumption and assume an active role in interpreting and integrating media into their own
lives (Luo, 2002). Unlike other theoretical perspectives, the UGT assumes that audiences are
responsible for choosing media to meet their desires and needs to achieve gratification
(Ruggiero, 2000). Many studies have shown the importance of informativeness to attitudes
toward online advertisements (Andrews, 1989).
Irritation. Irritation has the potential to divert attention from worthy social goals
(Galbraith and Crook, 1958), dilutes human experiences (Boorstin et al., 1974) and exploits
 human anxiety and fondly possessed hopes (Schudson, 2013). It can be caused by the Online video
organization of a website which confuses and distracts consumers (Chen, 1999). Gao and advertisement
Koufaris (2006) suggested that an unintended outcome from visiting a website may be a
user’s feeling of irritation. Ducoffe (1996) thought that consumers were likely to perceive
advertisements as an unwanted irritation if they used annoying, offensive, insulting or
overly manipulative techniques, and identified the annoyance or irritation they caused as
the main reason why people did not like advertisements.
Entertainment. McQuail (2010) indicated that the value of entertainment lies in its ability 843
to fulfill audiences' needs for escapism, diversion, aesthetic enjoyment or emotional release;
a view which is also extended from the UGT. Ducoffe (1996) also confirmed that the ability
of advertising to entertain can enhance the experience of advertising exchanges for
consumers. Other researchers have found that pleasant or likeable advertisements can have
positive impacts on brand attitudes (Mitchell and Olson, 1981).
Credibility. According to Brackett and Carr (2001) and Erkan and Evans (2016),
credibility refers to whether or not people trust the content of advertisement. It also indicates
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the trustworthiness or usefulness of advertising. It has been postulated that credibility has a
direct relationship with both advertising value and attitudes toward advertisements
(Eighmey, 1997).

Theory of reasoned action

TRA holds that a person’s behavior is determined by his or her behavioral intention, and
behavioral intention is determined by both the attitude of a person and the subjective norm
related to the behavior (Webster et al., 1994). The theory aims to explain the relationship
between attitudes and behaviors within human activity. So the attitude is defined as a
person’s positive or negative feeling about making an action (Webster et al., 1994). To
understand behavioral intent, which is seen as the main determinant of behavior, the TRA
focuses on a person’s attitudes toward that behavior as well as the subjective norms of
influential people and groups that could influence those attitudes (Ajzen and Fishbein, 1977).
In other words, TRA postulates that human behavior is driven primarily by behavioral
intention, which is a person’s readiness or desire to perform a given behavior.

Hypothesis development
Consumer attitude has been an important construct in marketing research for a long time,
and is still growing and developing as a focus of study. Ducoffe (1996) indicated that
entertainment, informativeness and irritation are the antecedent variables of advertising
value. These variables are also the antecedents of attitudes toward Web advertising.
The three antecedent variables might not be sufficient to predict attitudes toward
advertisements. Therefore, some other factors have been proposed as antecedent variables
of attitudes toward Web advertising. From among these additional factors, credibility was
added as a fourth perceptual antecedent (Eighmey, 1997). Moreover, Brackett and Carr
(2001) used these four variables and the relevant demographic variables to establish an
integrated Web advertising attitude model. Hence, we propose H1:
H1. The perceived entertainment, informativeness, irritation and credibility of
advertisement displayed while viewers are watching online videos affect viewers'
attitudes toward advertisement.
TRA was formulated in 1967 and was developed to examine the relationship between
attitudes and behavior. Considerable research has attempted to provide evidence of the
consistency of the relationship between behavior and attitudes in many studies (Ajzen,
 K 1991). The concept of attitude–intention–behavior postulates that an individual’s motivation
46,5 to engage in a behavior is defined by the attitudes that influence the behavior. Ajzen and
Fishbein (1977) thought that a person's intention is a function of his or her attitude toward
performing the behavior and of his or her subjective norm, in turn. Thus, a single act is
predictable from the attitude toward that act. Hence, there is a high correlation between
intention and behavior. Therefore, we propose H2 and the H3, respectively:
844 H2. The attitudes toward advertisement affects consumers’ intention to purchase while
watching online video advertisement.
H3. Consumers’ purchase intention affects their shopping behavior while watching
online video advertisement.
Koufaris (2002) indicated that intrinsic enjoyment can positively impact the use of computer-
mediated environments. It can also influence the use of e-mail (Taylor and Todd, 1995),
software (Webster et al., 1994) and Web browsing (Hoffman and Novak, 1996). Moreover,
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Koufaris thought a consumer can be distracted by online activities like e-mail, instant
messaging or other Web sites. Such distractions can limit online consumer concentration.
Moreover, concentration as a measure of flow has been found to positively influence the
overall experience of computer users (Hoffman and Novak, 1996) and their intention to use a
system repeatedly (Webster et al., 1994). In addition, Koufaris investigated the level of flow
while users browsed shopping websites. His findings indicated that flow was influenced not
only by the level of concentration but also by the level of enjoyment, perceived control and
Web skills. These factors were all related to the flow of an online environment and were
confirmed to be the antecedent variables of intention to return to websites (Koufaris, 2002).
Hence, while users are watching online videos, the level of flow might influence their
intention to be receptive to advertisements. We therefore propose H4:
H4. The perceived level of flow while users watch online video affects the purchase
intention and shopping behavior to be receptive to advertisement.

Materials and methods

Instrument development
Development of measures requires careful analysis, as these become the building blocks for
establishing valid relationships among the variables. In our study, multi-staged scientific
instrument development and validation procedures were used. In addition, by analyzing the
data using structural equation modeling (SEM), nomological networks between endogenous
and exogenous variables were examined (Hasani et al., 2016).
The measurement of constructs that predict attitudes follows Ducoffe (1996) and Tsang
et al. (2004). However, credibility is examined using the measurements of Brackett and Carr
(2001) and Tsang et al. (2004). The instruments for measuring attitude and behavior are based
on Tsang et al. (2004) and Gao and Koufaris (2006). The instruments for considering intention
are based on Tsang et al. (2004) (note: only one item in Tsang et al.’s study is utilized). The
measurement of flow is adopted from Ghani and Deshpande (1994) (see Appendix).

Main survey
We distributed the questionnaires on mySurvey (www.mysurvey.tw), which is a popular
site designed in Taiwan that provides survey services. We also released our survey on PTT
(telnet://ptt.cc), which is the most famous and popular BBS (bulletin board system) in
Taiwan. There are over one million registered users of PTT, consisting mainly of Taiwanese
 people around the world. Participation was voluntary and the survey completion process Online video
took approximately 10 min. advertisement
We received 382 responses. After removing the invalid questionnaires through a filtering
item in the survey, we were left with 336 usable questionnaires. Demographic data showed
that males made up 48.8 per cent of the sample, and 88.4 per cent of respondents were 15 to
34 years old. In all, 94.3 per cent of respondents had three or more years of on-line experience
(see Table I). 845
Results
SEM was used to perform both measurement and structural model analysis simultaneously.
The analysis validated the psychometric properties of the measures and was used to

Category Frequency (n = 336) (%)

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Gender
Males 164 48.8
Females 172 51.2
Age
15-24 185 55.1
25-34 112 33.3
35-44 39 11.6
Education
Senior high 17 5.1
Universities and colleges 202 60.1
Institute 117 34.8
Residence
Northern region 214 63.4
Central region 52 15.5
Southern region 57 17
Eastern region 5 1.5
Islands region 4 1.3
Foreign 4 1.3
Online experience
Years < 1 5 1.5
1 ≤ years < 2 6 1.8
2 ≤ years < 3 8 2.4
3 ≤ years < 4 33 9.8
4 or more years 284 84.5
Online time per day
Hours < 1 4 1.1
1 ≤ hours < 2 18 5.4
2 ≤ hours < 3 47 14
3 ≤ hours < 4 44 13.1
4 or more hours 223 66.4
Usage of online video while surfing internet
Seldom 0 0
Occasional 69 20.5
Often 192 57.2 Table I.
Every time 75 22.3 Demographic data
 K investigate nomological network relationships between constructs in the model. Data were
46,5 analyzed using AMOS 7.0.

Measurement model analysis

A confirmatory factor analysis was conducted to validate the psychometric properties of the
instruments. They were measured by examining whether the measurement model had an
846 acceptable goodness-of-fit and by investigating its unidimensionality, convergent and
discriminant validity and reliability. Overall goodness-of-fit for the model was confirmed
(Table II). The x 2/df was 1.565, which is below the desired threshold of 3.0. The RMSEA
was 0.049, which is below the 0.08 cut-off. All NFI (0.949) and CFI (0.981) were above their
corresponding cut-off value of 0.90. These results suggest that the measurement model
adequately fits the data.
Convergent validity was evaluated using three criteria (Fornell and Larcker, 1981):
(1) all indicator factor loadings should be significant at p < 0.05 and exceed 0.7;
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(2) composite reliabilities should exceed 0.7; and

(3) average variance extracted (AVE) by each construct should exceed the variance
due to measurement error for that construct.

As shown in Table II, all factor loadings exceeded 0.7 and were significant at p < 0.001.
Composite reliabilities ranged between 0.791 and 0.972, and AVE values were well above the
cut-off value of 0.50, which is greater than variance due to measurement error. Therefore, all
three conditions for convergent validity were met.
Discriminant validity was assessed by constraining the estimated correlation parameters
( f ij) between constructs to 1.0 and then performing a chi-squared difference test on the
values obtained for the constrained and unconstrained models. The chi-squared differences

Construct Items Loadings Cronbach’s a Composite reliabilities AVE

Entertainment ENT1 0.872 0.945 0.950 0.864

ENT2 0.943
ENT3 0.971
Informativeness IF1 0.805 0.884 0.887 0.723
IF2 0.858
IF3 0.886
Irritation IRT1 0.910 0.951 0.954 0.912
IRT2 0.998
Credibility CRD1 0.956 0.971 0.972 0.921
CRD2 0.979
CRD3 0.943
Attitudes ATT1 0.943 0.849 0.884 0.720
ATT2 0.763
ATT3 0.829
Flow FLO1 0.893 0.957 0.957 0.848
FLO2 0.903
FLO3 0.961
FLO4 0.925
Table II. Behavior BHV1 0.742 0.739 0.791 0.656
Results of BHV2 0.873
measurement model
analysis Note: Intention has only one item in Tsang et al. (2004)
 between these models were significant at p < 0.05, demonstrating clear discriminant Online video
validity among these constructs. As a supplementary assessment of discriminant validity, advertisement
inter-construct correlations were used (see Table II). All constructs were found to have a
stronger correlation with their own measures than to those of other constructs. All the
correlations between the constructs were less than 0.7 and less than the square root (see
Table III) value of AVE shown in the diagonal, representing appropriate discriminant
validity (Lucas et al., 1996). Finally, reliability was examined using Cronbach’s a and all
constructs showed a value of over 0.739, indicating an appropriate reliability of items used 847
for each construct.

Structural model analysis

Figure 1 shows the results of the structural model analysis, including the R2 and path
loadings for all hypothesized relationships. The model was found to have considerable
ability to explain behavior related to advertising displayed during the watching of online
videos. The fit statistics ( x 2/df = 1.838, NFI = 0.936, CFI = 0.970, RMSEA = 0.060) indicated
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that the model provided a good fit to the data. All goodness-of-fit statistics were above their
cut-off values.
All components of H1 significantly influenced Attitudes. Entertainment (H1a: l = 0.414,
p < 0.001), Informativeness (H1b: l = 0.196, p < 0.05), Irritation (H1c: l =
0.161, p < 0.05)
and Credibility (H1d: l = 0.173, p < 0.05) influenced Attitudes and explained its large
variance (R2 = 0.526). Attitudes (H2: l = 0.673, p < 0.001) showed infuence on Intention
(R2 = 0.437, p < 0.01), Intention was found to have a significant effect on Behavior (H3: l =

1. 2. 3. 4. 5. 6. 7. 8.
Entertainment Informativeness Irritation Credibility Attitudes Intention Behavior Flow

1. Entertainment 0.930
2. Informativeness 0.648 0.850
3. Irritation
0.388
0.326 0.955
4. Credibility 0.363 0.395
0.092 0.960
5. Attitudes 0.676 0.587
0.404 0.416 0.849
6. Intention 0.359 0.400
0.282 0.276 0.661 1
7. Behavior 0.586 0.497
0.439 0.191 0.635 0.622 0.810
8. Flow 0.287 0.178
0.218 0.087 0.206 0.260 0.283 0.92 Table III.
Inter-construct
Note: Intention has only one item in Tsang et al. (2004) correlation matrix

Entertainment

0.414

Informativeness
0.196 Intention Behavior
Attitudes 0.673 0.628
–0.161 2
(R = 0.526)
2
(R = 0.437) (R 2 = 0.492)
Irritation

0.173
0.171 0.187
Credibility Figure 1.
Structural model
Flow analysis
 K 0.628, p < 0.001) and Flow had a positive influence on Intention (H4a: l = 0.171, p < 0.05)
46,5 and Behavior (H4b: l = 0.187, p < 0.05). As hypothesized in H4, Intention explained 49.2 per
cent of variances in Behavior. A summary of hypothesis test results is shown in Table IV.

Discussion
Our study shows that the proposed model explained most of the variance in terms of
848 attitude toward advertisements on sites providing online video services, such as YouTube.
The results indicate that the model's appropriateness in the situation of online video
advertising is confirmed by the degree to which the findings are in accordance with those of
previous investigations in different areas, such as internet advertisements (Brackett and
Carr, 2001) and mobile advertisements (Tsang et al., 2004, Tai et al., 2016).
We found that all the constituent elements of H1, i.e. the perceived entertainment,
informativeness, irritation and credibility of advertisements displayed while viewers are
watching online videos affect viewer attitudes toward these advertisements, were
supported. The constructs have a strong explanatory effect on attitude, especially
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entertainment. Irritation was found to have a negative impact on attitudes. These findings
are consistent with previous research (Tsang et al., 2004).
In terms of its informativeness, our study verifies the UGT and confirms that audiences
are responsible for choosing media to meet their needs so as to achieve gratification.
Advertisements may be pleasant or likeable experiences for audiences. They can fulfill
audiences’ needs for escapism, diversion, aesthetic enjoyment or emotional release.
Advertisements provide credibility for audiences because viewers may trust the content of
these advertisements. However, some audiences may consider advertisements to be
annoying, offensive or irritating. This explains why many viewers tend not to like to watch
advertisements in the video.
Our findings reveal similar results in terms of flow. Flow does significantly influence
purchase intention and shopping behavior. Flow is an important factor of purchase intention
and shopping behavior in the online video advertising research model. People who watch
online video advertising may be absorbed by the information they are interested in and may
have intention to buy things or services (behavior). So when people are in a flow situation,
they may pay more attention on online video advertising. In other words, flow is a kind of
mental concentration in Web browsing or navigation (Erkan and Evans, 2016), especially in
online video advertising. So flow is an important factor to increase the customers’ purchase
intention when they watch online video advertising. Our study confirms the relationship
among flow, intention and behavior. For instance, Koufaris (2002) confirmed that flow had a
positive impact on the intention to return to shopping websites. Hsu and Lu (2004) also
showed that the flow experience could predict the intention to play online games. Liu et al.
(2009) investigated online e-learning users’ acceptance behaviors in three contexts, such as

Hypotheses Path Results

H1a(þ) Entertainment Attitudes Supported

H1b(þ) Informativeness Attitudes Supported
H1c(
) Irritation Attitudes Supported
H1d(þ) Credibility Attitudes Supported
Table IV. H2(þ) Attitudes Intention Supported
Summary of H3(þ) Intention Behavior Supported
hypothesis test H4a(þ) Flow Intention Supported
results H4b(þ) Flow Behavior Supported
 text-audio, text-audio-video and audio-video, and found that flow was positively correlated Online video
with intention to use the technology in the contexts of text–audio–video and audio–video. advertisement
This study reveals that attitudes influence purchase intention. Consumers with positive
attitudes on advertising may raise the intention to buy the goods or services. They may feel
that the advertising is informativeness, trust and enjoyable. These positive attitudes may
raise the intention of their shopping behaviors. When people have intention to shop after
watching online video advertising, they may have positive attitudes or enough confidence to 849
complete the purchase. This is why good advertising attracts people to watch it again and
again (repeatedly) because they generate good feelings for audiences when they watch the
advertising.

Theoretical contributions
There are several theoretical contributions from this research. First, this study reveals that
the attitudes of online video advertising influence shopping intention. When watching
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online video advertising, people have good impression. This means that the content of
advertising is attractive or reliable and consumers would pay attention to watch it. This
finding provides enough evidence to justify why good advertising is trustable for customer
not just only because of the impressiveness of the advertising but because of the trust or
reliability of the product or service.
Second, this study also justifies that flow plays an important role in shopping intention
and behavior. When people pay attention to watching online video advertising, they may be
absorbed by the product or service information. If people are willing to watch the
advertising, they may be attracted by the content and have intention or behavior to buy
things. In other words, this advertising may locate the potential target customers.
Finally, the research model of this study is based on the Brackett and Carr (2001) model
and combined with the TRA and flow theory. The results is consistent with the findings of
Brackett and Carr (2001). The online Web advertising factors have an influence on attitudes.
When people have enough information, enjoyment and trust when they watch online video
advertising, and hence, they may have positive attitudes for their shopping intention and
behavior. But if they feel this advertising is irritable, this may reduce the willingness to
watch and they may not have intention to buy the goods or services.
For researchers, this study provides a comprehensive model for online video
advertisements. This model was based on Brackett and Carr’s model, combining the UGT,
TRA and flow theory to develop an online video advertisement model. For future studies,
researchers can consider this model as a framework and use it to capture a more complete
picture of the relevant phenomena in their works.

Managerial implications
The purpose of online video advertising is to increase the sales of a product or service. So,
marketing managers may use different channels to demonstrate their advertising. Online
video advertising is now a popular way to deliver the product information to consumers. In
terms of the implications for practitioners, online video advertising managers can develop
their business strategies based on this study model. Managers may wish to rethink the
context of their advertising, and consider taking steps to increase its informativeness,
entertainment value and credibility, while simultaneously reducing the level of irritation for
viewers. When audiences have great experiences on watching these video advertisements,
they may have positive attitudes and have greater shopping intention to buy (behavior).
 K Limitation
46,5 Our study had several limitations. Choosing YouTube as the platform precludes the
possibility of representing user experiences and perceptions with other multifarious online
video websites. There are many online video sites using different types of advertising and
the advertisements used may have different underlying principles. Second, using different
descriptions of contexts in our survey research to simulate the actual use of the online video
850 websites might still give rise to disparity between reported behavior and behavior
corresponding to actual use. Third, the data were collected on the internet, which may have
resulted in sampling bias. The majority of respondents, for example, were students. Finally,
all the instruments adopted from previous studies might have semantic and linguistic biases
resulting from the translation from English to Chinese.

Conclusion
This study demonstrated an online video advertisement model and used YouTube as a
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platform to investigate consumer attitudes toward advertisements. The research model was
based on the Brackett and Carr (2001) Web Advertising Attitudes Model and was combined
with the TRA and the flow theory. This study investigated consideration of the factors
affecting attitudes toward advertisement and the influence on shopping intention and
purchase behavior. The findings indicate that entertainment, informativeness, irritation and
credibility have an influence on attitudes. Flow, on the other hand, influences shopping
intention and purchase behavior. When people pay attention on the advertising, they may be
interested in this advertising and have a chance to buy the product or service. Practitioners
can refer to the research findings for making their Web advertising strategy decision.
Researchers can consider this model as a framework for their future research.

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Corresponding author
Chia-Hui Huang can be contacted at: leohkkimo@gmail.com
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 Appendix Online video
advertisement

Construct Measurement items References

Entertainment The advertising is entertaining Ducoffe (1996)

The advertising is enjoyable
The advertising is pleasing
853
Informativeness The advertising is a good source of product Ducoffe (1996)
information
The advertising supplies relevant product
information
The advertising provides timely information
Irritation The advertising is annoying Ducoffe (1996)
The advertising is irritating
Credibility The advertising is credible Tsang et al. (2004), Brackett and
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The advertising is trustworthy Carr (2001)

The advertising is believable
Attitudes Please use the descriptive words listed below to Gao and Koufaris, (2006)
indicate your overall impression of the advertising
Bad 1 2 3 4 5 6 7 Good
Unfavorable 1 2 3 4 5 6 7 Favorable
Dislike 1 2 3 4 5 6 7 Like
Behavior What do you do when you receive an advertising Tsang et al. (2004)
Ignore or close it immediately
Watch/Read it occasionally
Watch/Read it after appearing too many times
Watch/Read it when I get time
Watch/Read it right away
How much do you watch/read the advertisement you
receive
Not at all
Watch about a quarter of a advertising
Watch about half of a advertising
Watch about three-quarters of a advertising
Watch the whole advertising
Intention I am willing to receive advertisement while Tsang et al. (2004)
watching online video:
Less than one advertisement a video
Two advertisement a video
Three advertisement a video
Over four advertisement a video
Unwilling to receive advertising
Flow During my last visit to YouTube.com and watch the Ghani and Deshpande (1994)
online video. . .
I was absorbed intensely in the activity
My attention was focused on the activity Table AI.
I concentrated fully on the activity List of model
I was deeply engrossed in the activity constructs and items

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